---
layout: page
title: Ecosystem
subtitle: Explore a rich ecosystem of libraries, tools, and more to support research and development of Deep Learning application across many fields and domains of application.
action: Get Started
action_url: /get_started
permalink: /ecosystem/

ecosystem_toolkits:
- title: GluonCV
  text: GluonCV is a computer vision toolkit with rich model zoo. From object detection to pose estimation.
  icon: /assets/img/visual.svg
  link: https://gluon-cv.mxnet.io
- title: GluonNLP
  text: GluonNLP provides state-of-the-art deep learning models in NLP. For engineers and researchers to fast prototype research ideas and products.
  icon: /assets/img/artificial-intelligence.svg
  link: https://gluon-nlp.mxnet.io/
- title: GluonTS
  text: Gluon Time Series (GluonTS) is the Gluon toolkit for probabilistic time series modeling, focusing on deep learning-based models.
  icon: /assets/img/line-graph.svg
  link: https://gluon-ts.mxnet.io/

ecosystem_other:
- title: Coach RL
  text: Coach is a python reinforcement learning framework containing implementation of many state-of-the-art algorithms, it supports MXNet as a back-end
  icon: /assets/img/coach_logo.png
  link: https://github.com/NervanaSystems/coach
- title: Deep Graph Library
  text: DGL is a Python package dedicated to deep learning on graphs supporting MXNet as a backend.
  link: https://www.dgl.ai/
- title: GluonFR
  text: Community-driven toolkit for Face Recognition and Face Detection
  link: https://gluon-face.readthedocs.io/en/latest/
- title: InsightFace
  text: State-of-the-art face detection and face recognition repository, including ArcFace loss and RetinaFace implementation
  link: https://github.com/deepinsight/insightface
- title: Keras-MXNet
  text: Keras-MXNet provides a backend support for the widely used high level API Keras.
  link: https://github.com/awslabs/keras-apache-mxnet
  icon: /assets/img/keras.png
- title: MXBoard
  text: MXBoard provides a set of APIs for logging MXNet data for visualization in TensorBoard.
  link: https://github.com/awslabs/mxboard
- title: MXFusion
  text: MXFusion is a modular deep probabilistic programming library. It lets you use state-of-the-art inference techniques for specialized probabilistic models.
  icon: /assets/img/mxfusion.png
  link: https://mxfusion.readthedocs.io/en/master/index.html
- title: MXNet Model Server
  text: Model Server for Apache MXNet (MMS) is a flexible and easy to use tool for serving deep learning models exported from MXNet or the Open Neural Network Exchange (ONNX).
  link: https://github.com/awslabs/mxnet-model-server
- title: Sockeye
  text: Sockeye is a sequence-to-sequence framework for Neural Machine Translation based on Apache MXNet Incubating. It implements state-of-the-art encoder-decoder architectures.
  link: https://awslabs.github.io/sockeye/
- title: TensorLy
  text: TensorLy is a high level API for tensor methods and deep tensorized neural networks in Python that aims to make tensor learning simple.
  icon: /assets/img/tensorly_logo.png
  link: http://tensorly.org/stable/home.html
- title: TVM
  text: TVM is an open deep learning compiler stack for CPUs, GPUs, and specialized accelerators. It supports a number of framework including MXNet.
  link: https://tvm.ai/about
  icon: /assets/img/tvm.png
- title: XFer
  text: Xfer is a library that allows quick and easy transfer of knowledge stored in deep neural networks implemented in MXNet.
  link: https://xfer.readthedocs.io/en/master/
  icon: /assets/img/xfer.png

---
<div class="ecosystem-page">
    <div class="row">
        <h2>D2L.ai</h2>
        <div class="row">
            <div class="col-4">
                <a href="http://d2l.ai/"><img src="{{'/assets/img/front.jpg' | relative_url}}"></a>
            </div>
            <div class="col-8">
                <p>A <a href="https://d2l.ai">deep learning book</a> with interactive jupyter notebooks, math formula,
                    and a dedicated forum for discussions.</p>
                <p>It offers an interactive learning experience with mathematics, figures, code, text, and discussions,
                    where concepts and techniques are illustrated and implemented with experiments on real data
                    sets.</p>
                <p>Each section is an executable Jupyter notebook. You can modify the code and tune hyperparameters to
                    get instant feedback to accumulate practical experiences in deep learning.</p>
                <p>The book is authored by <a href="https://www.astonzhang.com/">Aston Zhang</a>, Amazon Applied
                    Scientist UIUC Ph.D., <a href="http://zacklipton.com/">Zack C. Lipton</a>, CMU Assistant Professor
                    UCSD Ph.D.,
                    <a href="https://scholar.google.com/citations?user=Z_WrhK8AAAAJ&hl=en">Mu Li</a> Amazon Principal
                    Scientist CMU Ph.D. and <a href="https://alex.smola.org/">Alex J. Smola</a> Amazon VP/Distinguished
                    Scientist TU Berlin Ph.D.
                <p>D2L is used as a textbook or a reference book at Carnegie Mellon University, Georgia Institute of
                    Technology, the University of California Berkeley and many more university</p>
            </div>
        </div>
    </div>
    <br><br>
    <h2>Toolkits</h2>
    <div class="row">
        {%- for feature in page.ecosystem_toolkits -%}
        <div class="col-4">
            <div class="card">
                <a href="{{feature.link}}">
                    <div class="card-text">
                        <div class="card-header-title">
                            <h4>{{feature.title}}</h4>
                            <img src="{{feature.icon | relative_url}}">
                        </div>
                        <p class="card-summary">{{feature.text}}</p>
                    </div>
                </a>
            </div>
        </div>
        {%- endfor -%}
    </div>
    <br><br>
    <h2>Ecosystem</h2>
    <div class="row">
        {%- for feature in page.ecosystem_other -%}
        <div class="col-3">
            <div class="card">
                <a href="{{feature.link}}">
                    <div class="card-text">
                        <div class="card-header-title">
                            <h4>{{feature.title}}</h4>
                            <img src="{{feature.icon | relative_url}}">
                        </div>
                        <p class="card-summary">{{feature.text}}</p>
                    </div>
                </a>
            </div>
        </div>
        {%- endfor -%}
    </div>
    <br><br>
</div>